✍️ Text, Writing & Language

Frequently Asked Questions

Word Counter vs Readability Checker — what's the difference and when should I use each?

The Word & Character Counter gives you basic metrics: word count, character count (with/without spaces), sentence count, estimated reading time, and speaking time — use it for length targets. The Readability Score Checker goes deeper with Flesch-Kincaid grade level, reading ease score, syllable counts, and word statistics — use it when your audience has a specific reading level requirement (e.g., 8th grade for general public, 12th grade for academic). Use both as a pair: Word Counter for quantity, Readability Checker for quality.

How does the AI Token Counter estimate costs — which models does it support?

The AI Token Counter & Cost Estimator tokenizes text using model-specific tokenizers and estimates API costs for 12 models including GPT-4, GPT-3.5 Turbo, Claude 3, and others. It factors in both input and output token pricing, showing separate cost estimates for each. Use it before sending prompts to budget API calls, compare model costs for the same input, or verify you're staying within context window limits. All tokenization happens client-side.

AI Prompt Builder — does it actually improve LLM outputs, and how should I structure prompts?

The AI Prompt Builder enforces a structured format with role definition, task description, tone specification, and output format controls — elements that prompt engineering research consistently shows improve response quality across ChatGPT, Claude, and other LLMs. By separating the instruction from the context and specifying the desired output structure, you reduce ambiguity. The builder is a template, not a magic wand — the quality still depends on clear task specification, but the structure prevents common prompt failures like missing context or vague instructions.

Text Diff Checker — what algorithm does it use and can it handle large documents?

The Text Diff Checker uses a Longest Common Subsequence (LCS) diff algorithm that produces color-coded side-by-side comparisons with added, removed, and unchanged text clearly marked. It's designed for comparing code snippets, document revisions, and prose edits of a few thousand words — it handles typical diff workloads efficiently but is not optimized for extremely large files (10,000+ lines). For large codebase diffs, use a dedicated version control tool; for document comparisons, the Diff Checker is the right tool.

Does the Readability Checker handle non-English text — what about Turkish content?

The Readability Score Checker uses the Flesch-Kincaid formula, which is calibrated for English text. It calculates scores based on syllable counts, word counts, and sentence lengths — metrics that are language-specific. For English content, the results are reliable and widely used. For Turkish or other languages, the syllable-counting algorithm may produce less meaningful scores because syllable division rules differ. The word and character counts remain accurate for any Latin-script language, but treat the grade-level score as English-specific.